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Venetis K, Pescia C, Cursano G, Frascarelli C, Mane E, De Camilli E, Munzone E, Dellapasqua S, Criscitiello C, Curigliano G, Guerini Rocco E, Fusco N. The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy. Int J Mol Sci 2024; 25:5717. [PMID: 38891906 PMCID: PMC11172282 DOI: 10.3390/ijms25115717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.
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Affiliation(s)
- Konstantinos Venetis
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
| | - Carlo Pescia
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
- School of Pathology, University of Milan, 20122 Milan, Italy
| | - Giulia Cursano
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; (C.C.); (G.C.)
| | - Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; (C.C.); (G.C.)
| | - Eltjona Mane
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
| | - Elisa De Camilli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
| | - Elisabetta Munzone
- Division of Medical Senology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.M.); (S.D.)
| | - Silvia Dellapasqua
- Division of Medical Senology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.M.); (S.D.)
| | - Carmen Criscitiello
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; (C.C.); (G.C.)
- Division of New Drugs and Early Drug Development for Innovative Therapies, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giuseppe Curigliano
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; (C.C.); (G.C.)
- Division of New Drugs and Early Drug Development for Innovative Therapies, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Elena Guerini Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; (C.C.); (G.C.)
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy; (K.V.); (C.P.); (G.C.); (C.F.); (E.M.); (E.D.C.); (E.G.R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; (C.C.); (G.C.)
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Ji JH, Ahn SG, Yoo Y, Park SY, Kim JH, Jeong JY, Park S, Lee I. Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer-The BRAIN Study. Cancers (Basel) 2024; 16:774. [PMID: 38398165 PMCID: PMC10887075 DOI: 10.3390/cancers16040774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/26/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2- breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2- breast cancer.
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Affiliation(s)
- Jung-Hwan Ji
- Department of Surgery, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon 22711, Republic of Korea;
| | - Sung Gwe Ahn
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea;
| | - Youngbum Yoo
- Department of Surgery, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1 Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea;
| | - Shin-Young Park
- Department of Surgery, Inha University Hospital, College of Medicine, Incheon 22332, Republic of Korea;
| | - Joo-Heung Kim
- Department of Surgery, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin 16995, Republic of Korea;
| | - Ji-Yeong Jeong
- Department of AI Research, Neurodigm, Seoul 04790, Republic of Korea;
| | - Seho Park
- Division of Breast Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Ilkyun Lee
- Department of Surgery, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon 22711, Republic of Korea;
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Curigliano G, Dent R, Llombart-Cussac A, Pegram M, Pusztai L, Turner N, Viale G. Incorporating clinicopathological and molecular risk prediction tools to improve outcomes in early HR+/HER2- breast cancer. NPJ Breast Cancer 2023; 9:56. [PMID: 37380659 PMCID: PMC10307886 DOI: 10.1038/s41523-023-00560-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/06/2023] [Indexed: 06/30/2023] Open
Abstract
Stratification of recurrence risk is a cornerstone of early breast cancer diagnosis that informs a patient's optimal treatment pathway. Several tools exist that combine clinicopathological and molecular information, including multigene assays, which can estimate risk of recurrence and quantify the potential benefit of different adjuvant treatment modalities. While the tools endorsed by treatment guidelines are supported by level I and II evidence and provide similar prognostic accuracy at the population level, they can yield discordant risk prediction at the individual patient level. This review examines the evidence for these tools in clinical practice and offers a perspective of potential future risk stratification strategies. Experience from clinical trials with cyclin D kinase 4/6 (CDK4/6) inhibitors in the setting of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer is provided as an illustrative example of risk stratification.
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Affiliation(s)
- Giuseppe Curigliano
- European Institute of Oncology, IRCCS, Milan, Italy.
- Department of Oncology and Hemato-Oncology, University of Milano, Milan, Italy.
| | | | | | | | | | | | - Giuseppe Viale
- European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milano, Milan, Italy
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Utility of genomic platforms in treatment decisions in axilla-positive breast cancer. Clin Breast Cancer 2022; 22:634-641. [DOI: 10.1016/j.clbc.2022.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/12/2022] [Accepted: 07/24/2022] [Indexed: 11/19/2022]
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Sarhangi N, Hajjari S, Heydari SF, Ganjizadeh M, Rouhollah F, Hasanzad M. Breast cancer in the era of precision medicine. Mol Biol Rep 2022; 49:10023-10037. [PMID: 35733061 DOI: 10.1007/s11033-022-07571-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/01/2022] [Accepted: 05/05/2022] [Indexed: 01/02/2023]
Abstract
Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.
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Affiliation(s)
- Negar Sarhangi
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Shahrzad Hajjari
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Seyede Fatemeh Heydari
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Maryam Ganjizadeh
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Fatemeh Rouhollah
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Mandana Hasanzad
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. .,Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
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Siviengphanom S, Gandomkar Z, Lewis SJ, Brennan PC. Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs. Acad Radiol 2021; 29:1228-1247. [PMID: 34799256 DOI: 10.1016/j.acra.2021.09.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/14/2021] [Accepted: 09/26/2021] [Indexed: 12/19/2022]
Abstract
RATIONALE AND OBJECTIVES Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival. MATERIALS AND METHODS PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria. RESULTS Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed. CONCLUSION The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool.
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Affiliation(s)
- Somphone Siviengphanom
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia..
| | - Ziba Gandomkar
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia
| | - Sarah J Lewis
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia
| | - Patrick C Brennan
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia
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Bou Zerdan M, Ibrahim M, El Nakib C, Hajjar R, Assi HI. Genomic Assays in Node Positive Breast Cancer Patients: A Review. Front Oncol 2021; 10:609100. [PMID: 33665165 PMCID: PMC7921691 DOI: 10.3389/fonc.2020.609100] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/30/2020] [Indexed: 01/16/2023] Open
Abstract
In recent years, developments in breast cancer have allowed yet another realization of individualized medicine in the field of oncology. One of these advances is genomic assays, which are considered elements of standard clinical practice in the management of breast cancer. These assays are widely used today not only to measure recurrence risk in breast cancer patients at an early stage but also to tailor treatment as well and minimize avoidable treatment side effects. At present, genomic tests are applied extensively in node negative disease. In this article, we review the use of these tests in node positive disease, explore their ramifications on neoadjuvant chemotherapy decisions, highlight sufficiently powered recent studies emphasizing their use and review the most recent guidelines.
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Affiliation(s)
- Maroun Bou Zerdan
- Department of Internal Medicine, Naef K. Basile Cancer Institute, American University of Beirut Medical Center, Beirut, Lebanon
| | - Maryam Ibrahim
- Division of Internal Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Clara El Nakib
- Department of Internal Medicine, Naef K. Basile Cancer Institute, American University of Beirut Medical Center, Beirut, Lebanon
| | - Rayan Hajjar
- Department of Internal Medicine, Naef K. Basile Cancer Institute, American University of Beirut Medical Center, Beirut, Lebanon
| | - Hazem I. Assi
- Department of Internal Medicine, Naef K. Basile Cancer Institute, American University of Beirut Medical Center, Beirut, Lebanon
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Nakamura M, Takano A, Thang PM, Tsevegjav B, Zhu M, Yokose T, Yamashita T, Miyagi Y, Daigo Y. Characterization of KIF20A as a prognostic biomarker and therapeutic target for different subtypes of breast cancer. Int J Oncol 2020; 57:277-288. [PMID: 32467984 DOI: 10.3892/ijo.2020.5060] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/06/2020] [Indexed: 11/05/2022] Open
Abstract
The aim of the present study was to identify novel prognostic biomarkers and therapeutic targets for breast cancer; thus, genes that are frequently overexpressed in several types of breast cancer were screened. Kinesin family member 20A (KIF20A) was identified as a candidate molecule during this process. Immunohistochemical staining performed using tissue microarrays from 257 samples of different breast cancer subtypes revealed that KIF20A was expressed in 195 (75.9%) of these samples, whereas it was seldom expressed in normal breast tissue. KIF20A protein was expressed in all types of breast cancer observed. However, it was more frequently expressed in human epidermal growth factor receptor 2 (HER2)‑positive and triple‑negative breast cancer than in the luminal type. Moreover, KIF20A expression was significantly associated with the poor prognosis of patients with breast cancer. A multivariate analysis indicated that KIF20A expression was an independent prognostic factor for patients with breast cancer. The suppression of endogenous KIF20A expression using small interfering ribonucleic acids or via treatment with paprotrain, a selective inhibitor of KIF20A, significantly inhibited breast cancer cell growth through cell cycle arrest at the G2/M phase and subsequent mitotic cell death. These results suggest that KIF20A is a candidate prognostic biomarker and therapeutic target for different types of breast cancer.
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Affiliation(s)
- Masako Nakamura
- Department of Medical Oncology and Cancer Center, Shiga University of Medical Science, Otsu, Shiga 520‑2192, Japan
| | - Atsushi Takano
- Department of Medical Oncology and Cancer Center, Shiga University of Medical Science, Otsu, Shiga 520‑2192, Japan
| | - Phung Manh Thang
- Department of Medical Oncology and Cancer Center, Shiga University of Medical Science, Otsu, Shiga 520‑2192, Japan
| | - Bayarbat Tsevegjav
- Department of Medical Oncology and Cancer Center, Shiga University of Medical Science, Otsu, Shiga 520‑2192, Japan
| | - Ming Zhu
- Department of Medical Oncology and Cancer Center, Shiga University of Medical Science, Otsu, Shiga 520‑2192, Japan
| | - Tomoyuki Yokose
- Department of Pathology, Kanagawa Cancer Center, Yokohama, Kanagawa 241‑8515, Japan
| | - Toshinari Yamashita
- Department of Breast and Endocrine Surgery, Kanagawa Cancer Center, Yokohama, Kanagawa 241‑8515, Japan
| | - Yohei Miyagi
- Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa 241‑8515, Japan
| | - Yataro Daigo
- Department of Medical Oncology and Cancer Center, Shiga University of Medical Science, Otsu, Shiga 520‑2192, Japan
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